Dry air alters salt and water balance in the upper airways and increases the risks of COVID-19 among other respiratory diseases. We explored whether such upper airway variations in salt and water balance might alter respiratory droplet generation and potentially contribute to observed impacts of airway hydration on respiratory disease. In a randomized 4-arm study of 21 healthy human subjects we found that the breathing of humid air, the wearing of cotton masks, and the delivery of (sodium, calcium, and magnesium chloride) salt droplets sized to deposit in the nose, trachea, and main bronchi similarly reduce the exhalation of respiratory droplets by approximately 50% ([Formula: see text] ¡ 0.05) within 10 minutes following hydration. Respiratory droplet generation returns to relatively high baseline levels within 60–90 minutes on return to dry air in all cases other than on exposure to divalent (calcium and magnesium) salts, where suppression continues for 4–5 hours. We also found via a preliminary ecological regression analysis of COVID-19 cases in the United States between January 2020 and March 2021 that exposure to elevated airborne salt on (Gulf and Pacific) US coastlines appears to suppress by approximately 25%–30% ([Formula: see text] ¡ 0.05) COVID-19 incidence and deaths per capita relative to inland counties — accounting for ten potential confounding environmental, physiological, and behavioral variables including humidity. We conclude that the hydration of the upper airways by exposure to humidity, the wearing of masks, or the breathing of airborne salts that deposit in the upper airways diminish respiratory droplet generation and may reduce the risks of COVID-19 incidence and symptoms.

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Imposing stricter regulations for PM2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 μg/m3 in long-term PM2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual’s odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.